This study used a comparative approach to evaluate the impact of national energy performance improvement on corporate disclosure of Environmental (E), Social (S), and Governance (G) disclosures (ESG) in companies based in the United States and Europe. The research components included renewable energy, energy intensity, and energy savings. An extensive panel dataset comprising financial and performance data for 521 U.S. companies and 305 European enterprises spanning 11 distinct industries over 20 years was examined. This panel dataset contained comprehensive information on firms' energy performance, ESG indicators, and other critical variables, which were employed to investigate the correlation between enhancements in national energy performance and companies' ESG outcomes. The findings indicated that the factors influencing ESG disclosures in European and American companies differ. In Europe, energy savings significantly improved the ESG score, with an increase of approximately 12.5 units in the ESG index for every 1 billion dollars saved in energy. In contrast, in the United States, a reduction in energy intensity and an increase in the share of renewable energy resulted in improvements of 115 and 22.3 units, respectively, in the ESG index. These findings suggested that while both regions strongly emphasize energy and environmental policies, their approaches to achieving sustainability goals vary. Furthermore, each environmental, social, and governance improvement measure impacts various economic sectors differently. Therefore, companies must adopt strategies tailored to their industry to enhance their ESG performance. This study's findings had significant implications for policymakers, politicians, companies, and government agencies formulating a roadmap to promote the transition to renewable energy sources. The main innovation of this research lies in the comprehensive comparison of the ESG performance of European and American companies. This comparison enables the identification of best practices and successful models across two continents.

This essential guide empowers readers to leverage artificial intelligence (AI) as a tool for efficiency, financial viability, growth and innovation, and as a force for positive social and environmental impact, creating sustainable shared value for all stakeholders. It explores the transformative role of AI in advancing business sustainability and explains how technology and the use of AI, including generative AI (GenAI) and large language models (LLMs), can foster and promote sustainability initiatives as well as measure, report, and analyze key performance indicators in sustainability.

AI for Sustainable Business offers a strategic roadmap for integrating AI to foster responsible, long-term sustainable business practices. Using straightforward language, the book helps unpack the sometimes complex theoretical and practical concepts and procedures. As businesses increasingly recognize the importance of sustainability, the challenge lies in effectively integrating sustainable practices with advanced technologies. The book addresses this challenge by offering practical insights, frameworks, and case studies that illustrate the successful implementation of AI in various business contexts. It highlights the potential of AI to optimize resource use, reduce environmental impact, and improve corporate governance while maintaining a competitive advantage. This book enables businesses to make more informed decisions, optimize their operations, and promote sustainable practices, ultimately contributing to a more sustainable future by providing practical insights for leaders, managers, and policymakers.

This book should be a mandatory desktop reference for corporate directors, officers, advisors, legal counsel, accountants, and auditors who are engaged in sustainability factors of planning, performance, risk, reporting, and assurance. It also provides business leaders, academics, and students with a comprehensive understanding of how AI can be strategically integrated into business operations to enhance sustainability, drive innovation, and create long-term value.

In the past decade, interest in firms committed to environmental, social, and governance (ESG) sustainability has surged. This study analyzes the comparative influence of ESG sustainability performance and economic sustainability performance (ESP) on investment efficiency. Using a sample of 49,924 firm-year observations from 2010 to 2017, we categorize firms into those in shareholder-oriented and stakeholder-oriented countries. Findings indicate that social and environmental dimensions of sustainability contribute more significantly to improving investment efficiency than the economic dimension. The study highlights the differing roles of shareholder primacy in the U.S. and stakeholder primacy in the E.U. in shaping ESG investment strategies, offering insights for policy, practice, and future research.

Most traditional fraud detection systems primarily focus on financial criteria to identify financial fraud, often overlooking the potential for fraudulent companies to engage in various types of non-financial misconduct. Recent studies have predominantly highlighted the significance of financial data as the sole indicator of fraud, neglecting the exploration of non-financial or Environmental, Social, and Governance (ESG) metrics as supplementary predictors. This research aims to enhance fraud prediction by integrating financial and ESG data through sophisticated machine learning and deep learning models. It examines the effectiveness of supervised machine learning and deep learning algorithms in detecting financial fraud over a 10-year period ending in 1401. This study innovatively demonstrates that a hybrid model, which combines financial and non-financial criteria, yields superior predictive accuracy for financial fraud than models based solely on financial data.The results of this study, addressing the first research question, indicate that among various machine learning and deep learning algorithms, the classification or bagging algorithm demonstrated superior efficiency. Furthermore, in response to the second research question, it was found that the dataset encompassing all features—integrating both financial and non-financial data—outperformed those datasets limited to either financial or non-financial data alone. The research results indicated that the bagging machine learning algorithms act the best with combined feature set including financial and ESG metrics combined. The adoption of the proposed model significantly improves the accuracy and effectiveness of fraud detection systems.

Purpose: This paper aims to investigate how integrated reporting quality (IRQ), as well as comprehensive disclosure score (CDS) (i.e. incorporating integrated and sustainable reporting quality), impacts value creation differently between companies operating under mandatory versus voluntary adoption of these reporting frameworks. Design/methodology/approach: The sample comprises 1,195 firm-year observations (international data set) from 2018 to 2022, which are divided into groups based on mandatory vs voluntary adoption of the international integrated reporting framework (IIRF) and Sustainability Accounting Standards Board (SASB). Furthermore, regression analysis is used in the analyses. Findings: The findings revealed a significant and positive relationship between IRQ and value creation on a global scale. In addition, unlike voluntary adoption of the IIRF, mandatory adoption of it showed a significant and positive relationship between IRQ and value creation. Furthermore, an increase in the CDS had a greater impact on value creation compared to IRQ. Finally, in contrast to companies with voluntary adoption of both IIRF and SASB, companies with mandatory adoption of them exhibited a significant and positive relationship between these reports and value creation. Practical implications: The findings have practical implications for various stakeholders. First, by enhancing the awareness and understanding of integrated reporting and sustainability reporting among users, these results can facilitate more informed economic decision-making and enable a more accurate assessment of a company's potential for value creation. Second, these findings can contribute to the development of more effective and tailored reporting guidelines that align with the nuances of value creation dynamics in different contexts. Ultimately, this research can lead to improvements in reporting practices and regulatory frameworks, benefiting both companies and their stakeholders. Social implications: The study's social implications are significant as it offers insights into the global debate surrounding the adoption of the IIRF and the objectives of the merger involving the Value Reporting Foundation and the International Financial Reporting Standards Foundation. The findings provide a concrete basis for evaluating the value of adopting the IIRF and inform discussions on the future of reporting standards and practices. Originality/value: Furthermore, it stands as one of the pioneering endeavors to investigate the value creation aspects of CDS. These unique aspects make a substantive contribution by expanding the frontiers of knowledge in the realm of corporate reporting and financial implications, offering novel insights and opportunities for further research in this crucial domain.

This paper aims to analyze the moderating role of debt maturity and the mediating role of information asymmetry in the relationship between financial leverage and investment efficiency and to compare the results between firms domiciled in emerging and those headquartered in developed countries. Using two proxies for investment efficiency, the results showed that debt maturity moderates the association between financial leverage and investment efficiency at both levels of developed and emerging countries. This paper also confirmed that information asymmetry carries the influence of financial leverage to investment efficiency only for those enterprises headquartered in emerging countries.

Background: Health system performance is a multifaceted concept that encompasses various dimensions of a nation's healthcare infrastructure. This study aims to assess and rank the performance of health systems across different regions of the world.

Methodology: We employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method in 2023 to evaluate and rank the health system performance of 31 countries across six geographical regions. Our evaluation included six general categories and twelve indicators related to health, finance, and the COVID-19 pandemic. The final weights for these indicators were determined using the Three-scale method and the Entropy-weighting method. Additionally, we categorized health system performance into three groups: high, moderate, and low. Hierarchical clustering of health system performance scores was conducted using SPSS software (version 26).

Results: Luxembourg emerged as the only high-performing health system, while Qatar and the Netherlands fell into the moderate-performance group. Other countries exhibited low-performing health systems. Notably, within the low-performance group, the United States of America, Australia, Singapore, Canada, England, and Germany achieved relatively better rankings. Conversely, Yemen, Egypt, Afghanistan, and Bolivia ranked lowest in terms of health system performance.

Conclusion: Contrary to the assumption that higher health spending guarantees improved performance, the experience of COVID-19 among high-income countries revealed mixed results. Strengthening resilience, investing in public health systems, and ensuring sustainable financial resources are crucial for enhancing health system performance.

We examine whether the audit regulation of disclosing key audit matters (KAM) provides value-relevant information to short sellers as informed investors. The theoretical underpinning for examining short sellers' ability and incentives to use KAM disclosures in their stock valuation implications is based on a prediction theory and a skilled information processing theory of short sellers. Using a sample of expanded auditor's reports from UK-listed firms during the 2010–2017 period and hand-collecting a dataset of KAM disclosures, we find no evidence that the short interest is different for the period before than after the U.K.'s expanded auditor's report regulation. However, in our cross-sectional tests, we find that KAM disclosures have a marginal effect on short interest and a positive association between short interest and unexpected and severe KAM disclosures. We conclude that, except for severe KAM that is value-relevant to sophisticated investors, the disclosures in the expanded auditor's report have no valuation implications for short sellers. Our results are robust in examining the reactions of the financial market and analysts to KAM disclosures and addressing potential endogeneity concerns.

The primary concern of research in the area of fraud risk relevant to sustainability reporting lies in understanding the potential for fraudulent or misleading reporting practices and developing strategies and tools to identify and prevent such behaviors. Taking into consideration that significant research has yet to be conducted on fraud risk models associated with sustainability reporting, this study represents an innovative contribution. It uses a mixed-methods approach to design a fraud risk model based on sustainability reporting. Given its type and approach, this research does not posit any hypotheses involving thematic analysis and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. This study is exploratory and applied, aiming to design a model through a mixed-methods methodology. Therefore, the research is hypothesis-free and instead utilizes a qualitative sample of experts and academics in the field of accounting from Iran and Denmark. The DEMATEL technique identifies key external and internal factors that significantly impact sustainability reporting, including comprehensive internal controls, strong governance and oversight, training and awareness, the utilization of technology, and data analytics. The influence of stakeholders, third-party audits, and the credibility of sustainability reports emerges as particularly significant in this context, exceeding the impact of other factors. These findings underscore that stakeholders, third-party audits, and report credibility to play a more prominent role in shaping sustainability performance compared to other considerations. This would imply that such variables remain key drivers in the perceptiveness and effectiveness of sustainability performance.

With the expansion of business activities around the world and the importance of sustainability in various fields, corporate sustainability has become a strategic imperative for management plans and investment decision. Therefore, this study focuses on examining the contribution of sustainability variables, i.e., economic, social, and environmental (ESG), to corporates profitability at 5936 companies distributed globally in an industry sectors using the data mining methods. The data extracted from Thomson Reuters database (ASSET4 ESG) for the period of 2002-2017 was used for modelling. Different algorithms, such as decision tree, support vector machine, and Na & iuml;ve Bayes, were used for modelling. Since the current study uses a multi-class classification, the Kappa criterion was used to assess the quality of the classification algorithm. The results of the study confirmed that none of the sustainability dimensions had a negative impact on corporate profitability.